Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	122,694
2	South Dakota	114,254
3	Iowa	91,376
4	Wisconsin	91,351
5	Utah	90,129
6	Rhode Island	88,593
7	Nebraska	88,551
8	Tennessee	88,209
9	Idaho	81,213
10	Kansas	80,561
11	Indiana	79,629
12	Arkansas	79,159
13	Wyoming	78,735
14	Arizona	78,540
15	Illinois	78,482
16	Montana	78,153
17	Oklahoma	77,905
18	Alabama	77,417
19	Nevada	76,529
20	Mississippi	75,750
21	Minnesota	75,416
22	New Mexico	70,820
23	Missouri	70,523
24	Louisiana	70,265
25	Alaska	66,027
26	Florida	64,816
27	Georgia	64,304
28	Texas	64,291
29	South Carolina	63,719
30	Kentucky	63,689
31	California	63,570
32	Ohio	62,879
33	Delaware	62,746
34	Colorado	60,513
35	Massachusetts	57,628
36	New Jersey	56,478
37	Connecticut	55,246
38	North Carolina	55,139
39	Michigan	54,557
40	New York	53,899
41	Pennsylvania	53,097
42	West Virginia	51,983
43	Maryland	47,956
44	Virginia	43,572
45	District of Columbia	42,743
46	Puerto Rico	35,361
47	New Hampshire	35,295
48	Washington	34,631
49	Oregon	28,334
50	Maine	19,762
51	Hawaii	15,678
52	Vermont	12,881

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,857
2	Arizona	1,488
3	Oklahoma	1,028
4	South Carolina	994
5	California	960
6	Arkansas	822
7	Louisiana	815
8	Texas	808
9	Delaware	762
10	Utah	749
11	West Virginia	713
12	Alabama	689
13	New Hampshire	688
14	Nevada	662
15	Kentucky	652
16	Georgia	648
17	Connecticut	640
18	Tennessee	634
19	Massachusetts	629
20	New York	627
21	North Carolina	583
22	Ohio	579
23	Florida	578
24	Mississippi	578
25	Wyoming	573
26	Idaho	530
27	Virginia	513
28	Kansas	497
29	Indiana	496
30	New Mexico	495
31	Missouri	485
32	Montana	479
33	Wisconsin	471
34	Nebraska	470
35	Pennsylvania	469
36	South Dakota	469
37	Iowa	465
38	Colorado	464
39	Washington	456
40	Illinois	442
41	Minnesota	439
42	New Jersey	422
43	Maryland	363
44	Alaska	343
45	Maine	327
46	District of Columbia	310
47	Michigan	301
48	North Dakota	263
49	Oregon	249
50	Vermont	186
51	Puerto Rico	144
52	Hawaii	71

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,182
2	New York	1,969
3	Massachusetts	1,847
4	Rhode Island	1,765
5	North Dakota	1,753
6	Connecticut	1,736
7	South Dakota	1,710
8	Mississippi	1,671
9	Louisiana	1,642
10	Illinois	1,465
11	Michigan	1,361
12	Pennsylvania	1,295
13	Indiana	1,286
14	Arizona	1,285
15	Arkansas	1,271
16	Iowa	1,267
17	New Mexico	1,238
18	District of Columbia	1,134
19	South Carolina	1,067
20	Tennessee	1,054
21	Nevada	1,053
22	Florida	1,033
23	Georgia	1,010
24	Maryland	1,006
25	Texas	998
26	Missouri	997
27	Alabama	996
28	Kansas	994
29	Minnesota	978
30	Delaware	972
31	Montana	941
32	Wisconsin	922
33	Nebraska	896
34	Colorado	878
35	Idaho	824
36	West Virginia	804
37	Wyoming	801
38	Ohio	791
39	California	695
40	Kentucky	692
41	North Carolina	675
42	Oklahoma	649
43	Virginia	608
44	New Hampshire	582
45	Puerto Rico	489
46	Washington	474
47	Utah	409
48	Oregon	367
49	Alaska	285
50	Maine	274
51	Vermont	238
52	Hawaii	202

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	29
2	Mississippi	15
3	Tennessee	14
4	Wyoming	14
5	Arizona	13
6	Indiana	12
7	West Virginia	12
8	Arkansas	11
9	Massachusetts	11
10	Missouri	11
11	Louisiana	10
12	Michigan	10
13	Montana	10
14	Illinois	9
15	Nevada	9
16	New Mexico	9
17	Connecticut	8
18	North Dakota	8
19	Pennsylvania	8
20	California	7
21	Kentucky	7
22	New Jersey	7
23	New York	7
24	South Carolina	7
25	Maryland	6
26	Nebraska	6
27	Ohio	6
28	Wisconsin	6
29	Delaware	5
30	Iowa	5
31	New Hampshire	5
32	Texas	5
33	Vermont	5
34	Colorado	4
35	District of Columbia	4
36	Florida	4
37	Idaho	4
38	Minnesota	4
39	North Carolina	4
40	South Dakota	4
41	Georgia	3
42	Oklahoma	3
43	Oregon	3
44	Puerto Rico	3
45	Washington	3
46	Kansas	2
47	Maine	2
48	Utah	2
49	Virginia	2
50	Alabama	0
51	Alaska	0
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	274,872	1	99
Dewey	South Dakota	221,656	2	99
Norton	Kansas	221,227	3	99
Lincoln	Arkansas	218,980	4	99
Bon Homme	South Dakota	214,751	5	99
Davidson	Tennessee	103,633	292	90
Richland	South Carolina	66,308	1609	48
York	South Carolina	58,495	1996	36
Orange	California	57,263	2051	34
Pierce	Washington	31,965	2840	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Iron	Wisconsin	6,154	5	99
Davidson	Tennessee	864	1802	42
Richland	South Carolina	827	1863	40
York	South Carolina	669	2133	32
Orange	California	606	2241	28
Pierce	Washington	404	2591	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.